Image search method and device

ABSTRACT

An image search method that is robust and fast (with computational complexity of logarithmic order relative to the number of models). The image search includes extracting a plurality of regions from one or more model images and normalizing the regions as standard regions; setting a specific region in each normalized standard region and classifying the plurality of standard regions under two or more subsets on the basis of a feature of the specific region; iteratively performing an operation of setting another specific region at a location different from that of the aforementioned specific region in each standard region classified in each subset and classifying the plurality of standard regions under still more subsets on the basis of a feature of the other specific region; and outputting the locations of the specific regions in the standard regions in the respective classifications and the features of the specific regions in the classifications.

TECHNICAL FIELD

The present invention relates to an image search method and apparatusfor construction of a database permitting a fast image search, and forthe fast search itself.

BACKGROUND ART

Calculation of small regions in correspondence relationship between aplurality of images is a significant issue for various image processingapplications such as object recognition, 3D information reconstruction,and image searching. An image recognition means configured to extractlocal regions in images in a normalized state invariant to affinetransformation and rotation transformation (which will be referred tohereinafter as affine-invariant regions) and to use correspondencerelationship between the affine-invariant regions has the advantage thata change of a viewpoint relative to a recognition object can begeometrically modeled. Since it utilizes the local affine-invariantregions, it also has the advantage of high adaptability for partialhiding of the recognition object.

[Non-patent Document 1] D. G. Lowe “Distinctive image features fromscale-invariant keypoints” Int. J. Compt. Vision, 60(2): 91-110, 2004[Non-patent Document 2] J. Mates, O. Chum, M. Urban, and T. Pajdla“Robust Wide Baseline Stereo from Extremal Regions” BMVC02, 2002

These techniques are generally implemented by the following three-stepprocessing (cf. FIG. 8). (1) To extract affine-invariant regions fromone or more model images and a search object image (sample image). (2)To calculate correspondences of the extracted affine-invariant regionson the basis of local information. (3) To examine the correspondencescalculated in the above step (2), using global information.

DISCLOSURE OF THE INVENTION

Robustness and execution speed are also significant issues herein in thecalculation of correspondences between the affine-invariant regions inthe above step (2). For example, where a plurality of objects arerecognized, and when a method adopted is to sequentially comparecoincidence between affine-invariant regions extracted from a pluralityof model images and affine-invariant regions extracted from a searchobject image as shown in FIG. 9, the computational load also linearlyincreases with increase in the number of model images (i.e., the numberof affine-invariant regions extracted therefrom), and it is fatal toreal-time applications. Therefore, an object of the present invention isto provide an image search method and apparatus being robust and fast(with computational complexity of logarithmic order relative to thenumber of models).

In [Non-patent Document 1] above, an image histogram is used as an indexto find correspondences between the affine-invariant regions and therebyimplement comparison between two images. However, since the comparisonis made including the background part other than parts desired as searchobjects, it is infeasible to find accurate correspondences betweenaffine-invariant regions, if the background part exists at a largeratio.

An image search method as defined in claim 1 is characterized bycomprising: a normalization step of extracting a plurality of regionsfrom one or more model images and normalizing the regions as standardregions; a classification step of setting a specific region in eachnormalized standard region and classifying the plurality of standardregions under two or more subsets on the basis of a feature of thespecific region; a recursive classification step of iterativelyperforming an operation of setting an other specific region at alocation different from that of the aforementioned specific region ineach standard region classified in each subset and classifying theplurality of standard regions under still more subsets on the basis of afeature of the other specific region; and an output step of outputtingthe locations of the specific regions in the standard regions in therespective classifications and the features of the specific regions inthe classifications.

The invention as defined in claim 2 is the image search method accordingto claim 1, wherein the features of the specific regions used in theclassifications are luminance information about the specific regions.

The invention as defined in claim 3 is the image search method accordingto claim 1 or 2, wherein the normalization step comprises dividing astandard region into a background part and a search part, and whereinwhen the location of the specific region in the classification step orin the recursive classification step is in the background part, thespecific region is included in all the subsets in the classification.

The invention as defined in claim 4 is the image search method accordingto any one of claims 1 to 3, wherein the normalization step comprisesnormalizing a region possessing such a property that a shape can benormalized regardless of an affine transformation thereof, as a standardregion.

The invention as defined in claim 5 is the image search method accordingto any one of claims 1 to 3, wherein the normalization step comprisesnormalizing a region possessing such a property that a shape can benormalized regardless of a rotation transformation thereof, as astandard region.

The invention as defined in claim 6 is the image search method accordingto any one of claims 1 to 5, further comprising: an input step ofinputting a predetermined region resulting from normalization of aregion extracted from a search object, as a detection object; and asearch step of performing a search to determine to which terminal subsetthe predetermined region belongs, based on an output result in theoutput step, and thereby finding a correspondence between thepredetermined region and a subset of the standard regions.

The invention as defined in claim 7 is the image search method accordingto claim 6, wherein the search step comprises finding the correspondencebetween the predetermined region and the subset of the standard regions,in consideration of a location deviation from the standard region in thenormalization for the predetermined region in the input step.

An image search apparatus as defined in claim 8 is characterized bycomprising: normalization means for extracting a plurality of regionsfrom one or more model images and normalizing the regions as standardregions; classification means for setting a specific region in eachnormalized standard region and classifying the plurality of standardregions under two or more subsets on the basis of a feature of thespecific region; recursive classification means for iterativelyperforming an operation of setting an other specific region at alocation different from that of the aforementioned specific region ineach standard region classified in each subset and classifying theplurality of standard regions under still more subsets on the basis of afeature of the other specific region; and output means for outputtingthe locations of the specific regions in the standard regions in therespective classifications and the features of the specific regions inthe classifications.

The invention as defined in claim 9 is the image search apparatusaccording to claim 8, wherein the features of the specific regions usedin the classifications are luminance information about the specificregions.

The invention as defined in claim 10 is the image search apparatusaccording to claim 8 or 9, wherein the normalization means divides astandard region into a background part and a search part, and whereinwhen the location of the specific region in the classification by theclassification means or in the recursive classification by the recursiveclassification means is in the background part, the specific region isincluded in all the subsets in the classification.

The invention as defined in claim 11 is the image search apparatusaccording to any one of claims 8 to 10, wherein in the normalization bythe normalization means a region possessing such a property that a shapecan be normalized regardless of an affine transformation thereof, isnormalized as a standard region.

The invention as defined in claim 12 is the image search apparatusaccording to any one of claims 8 to 10, wherein in the normalization bythe normalization means a region possessing such a property that a shapecan be normalized regardless of a rotation transformation thereof, isnormalized as a standard region.

The invention as defined in claim 13 is the image search apparatusaccording to any one of claims 8 to 12, further comprising: input meansfor inputting a predetermined region resulting from normalization of aregion extracted from a search object, as a detection object; and searchmeans for performing a search to determine to which terminal subset thepredetermined region belongs, based on an output result by the outputmeans, and thereby finding a correspondence between the predeterminedregion and a subset of the standard regions.

The invention as defined in claim 14 is the image search apparatusaccording to claim 13, wherein in the search by the search means thecorrespondence between the predetermined region and the subset of thestandard regions is found in consideration of a location deviation fromthe standard region in the normalization for the predetermined region inthe input step.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an explanatory drawing showing a schematic process of learningin an image search method and apparatus of the present invention.

FIG. 2 is an explanatory drawing showing a schematic process ofsearching in the image search method and apparatus of the presentinvention.

FIG. 3 is a flowchart showing an overall flow in the image search methodand apparatus of the present invention.

FIG. 4 is an explanatory drawing schematically showing geometricdeviation in extraction of an affine-invariant region.

FIG. 5 is an explanatory drawing schematically showing modeling ofgeometric deviation in extraction of an affine-invariant region.

FIG. 6 is a flowchart showing a learning subroutine in the image searchmethod and apparatus of the present invention.

FIG. 7 is a flowchart showing a search in the image search method andapparatus of the present invention.

FIG. 8 is an explanatory drawing showing a state in which local regionsin images are extracted in an invariant state to affine transformationand rotation transformation.

FIG. 9 is an explanatory drawing showing matching between extractedlocal images.

BEST MODE FOR CARRYING OUT THE INVENTION

An image search apparatus of the present invention has an input unit forinputting an object image as a search object and model images to be usedas a database for comparison with the object image; a calculation unitfor carrying out calculation to determine an affine-invariant region andothers for an input image, and comparison for a search; an output unitfor outputting a constructed database or search result; a memory unitfor storing programs necessary for the calculation, intermediatecalculation result, the constructed database, etc.; and so on.

The input unit can be a camera, a scanner, or an input/output drive forinputting an image as data. The calculation unit can be a CPU orGPU•ROM•RAM, or the like. The output unit can be a monitor display, aprinter, or an input/output drive. The memory unit can be a hard disk, aRAM, or one of other storage devices.

An image search method (database construction method) in the presentembodiment will be summarized below. The following description willconcern an example where road signs are dealt with as images. It is alsonoted that the following will describe the present invention as acorrespondence calculation technique of affine-invariant regions but thescope of application of the invention is not limited to theaffine-invariant regions. The present invention is widely applicable tocorrespondence calculations of images even in the other techniques, suchas the raster scan, as long as the images dealt with are imagesnormalized in fixed size.

First, let us consider a comparison technique of affine-invariantregions capable of eliminating influence of the background part. It isassumed herein that the background part is unknown for anaffine-invariant region extracted from a sample image (search objectimage) (which will be referred to hereinafter as a sampleaffine-invariant region) and that the background part is known for aplurality of affine-invariant regions extracted from model images as asource of a search database (which will be referred to hereinafter asmodel affine-invariant regions).

Since the background part is different among the model affine-invariantregions, we cannot uniformly handle evaluation of similarity with thesample affine-invariant region for all the model affine-invariantregions. For this reason, it is necessary to perform one-to-oneevaluation of similarity of the sample affine-invariant region with allthe model affine-invariant regions. This operation linearly increasescomputational complexity against the number of models, and is thus fatalto real-time applications handling a large number of models.

Then the present embodiment uses pixel values of the affine-invariantregions as indices of comparison and alternately performs selection ofan optimal interest pixel for refinement of a model affine-invariantregion corresponding to the sample affine-invariant region, and actualrefinement, while excluding a model whose selected pixel is in thebackground part, from targets of refinement in the operation, wherebythe present embodiment realizes correspondence calculation fast androbust without influence of the background even in the case where thenumber of models is large. FIGS. 1 and 2 show conceptual drawings of thepresent invention.

FIG. 1 shows construction of a database as a basis of comparison when anobject image is given. In this example there are three modelaffine-invariant regions (affine-invariant regions normalized andextracted from model images) as a source of a database as shown on theupper left in the drawing. It is noted that a practical affine-invariantregion is not always an entire sign, but, for convenience' sake ofdescription, the model affine-invariant regions in the drawing are thewhole regions of a road sign indicating a level difference, a road signindicating a signal, and a road sign of mark X. In this case, thebackground part except for the signs is preliminarily set as abackground. First, a specific region (region indicated by □ in thetable) is set for each model image (standard region) and three modelimages (standard regions) are classified under two or more subsets,based on luminance information of the specific region.

However, if the specific region falls in the background in a model image(standard region), the model image must be included in all theclassified subsets. This classification step results in constructing twosubsets as in the middle column in the drawing. Then another specificregion is set at a location different from that of the foregoing, ineach subset and the model images (standard regions) are furtherrecursively classified into still more subsets, based on luminanceinformation of the specific region. Finally, this recursiveclassification step results in constructing a total of four subsets eachincluding one element, as in the right column in the drawing.

A tree-shaped database is constructed in this manner and, when a searchobject image is inputted based on this database, it is determined withwhich model image the search object image coincides. FIG. 2 is a drawingshowing an example of the search. The two images on the leftmost in FIG.2 are examples (two types) of sample affine-invariant regions extractedfrom a search object image (affine-invariant regions normalized andextracted from the object image). The search object image also containsthe background in addition to the road signs. A specific region (regionindicated by □ in the table) is set in each object image (standardregion) according to the database and to which subset each object imagebelongs is sequentially determined (branching determination), based onluminance information of the specific region.

However, since the object image has the background as described above,when the specific region is in the background, the determination onbelonging to which subset (branching determination) is made based on theluminance information of the background. Nevertheless, this poses noproblem because the construction of the database was based on thefollowing rule: if the specific region in the model image is in thebackground, the standard region must be included in all subordinatesubsets.

The method will be described below based on a flowchart. FIG. 3 is theflowchart showing the entire flow. The first step 300 is to extractregions possessing such a property that a shape can be normalizedregardless of affine transformation (or rotation transformation), from aplurality of model images whose background region is known, and tonormalize each region into a rectangle (or a circle or the like) offixed size (referred to as a standard region). This processing can beimplemented by a plurality of well-known techniques. The number ofnormalized regions extracted herein is defined as N, and the set thereofas S={s₁, s₂, . . . , s_(N)}. If the feature region extraction and thenormalization are carried out by combination of a plurality oftechniques or parameters, the processing of step 305 and subsequentsteps is carried out independently for each of the respective techniquesor parameters.

The next step 305 is to examine a pixel value ν at a pixel location xfor ∀A∈S. The sample affine-invariant region corresponding to A containssome noise and does not perfectly coincide with A. For a robustcorrespondence search, it is necessary to model the pixel value ν at thepixel location x as a probability function p_(x,A)(ν) in considerationof influence of the noise. Consideration is given herein to influence ofgeometric deviation on the occasion of extraction of theaffine-invariant regions, and optical deviation.

The cause of the geometric deviation is error in an affinetransformation matrix for normalization due to image quantization lossor other factors on the occasion of extraction of the affine-invariantregions. Supposing geometric deviation amounts for respective pixelsfollow a two-dimensional Gaussian distribution, a probabilitydistribution thereof can be expressed as a covariance matrix and can beexperimentally calculated by making use of a plurality of image pairswhose H matrix is known. FIG. 4 schematically shows probabilitydistributions of geometric deviation where an affine-invariant region isextracted by respective techniques in [Non-patent Document 2] describedabove. In the drawing (a) shows a case of the technique based on theregion covariance matrix, (b) a case of the technique based onbi-tangent, and (c) a case of the technique based on concavity.Directions and magnitudes of ellipses indicate directions and magnitudesof deviations.

The probability p_(x,A)(ν) of observing the pixel value ν at the pixellocation x of A is represented by the following equation using l_(x) asa probability distribution of geometric deviation at the pixel locationx and a region Ω_(ν,A) in A where the pixel value is ν.

p _(x,A)(ν)=∫_(Ω) _(ν,A) l _(x) dΩ  [Mathematical Expression 1]

FIG. 5 includes (a) and (b) schematically showing modeling of geometricdeviation. In FIGS. 5 (a) and (b) show distributions of probabilityp_(x,A)(ν), wherein (a) shows the geometric deviation l_(x) estimatedfor the pixel location x and (b) the probability p_(x,A)(ν) for thepixel value ν. In FIG. 5 (c) shows a case further taking account ofoptical deviation in comparison with (b).

The optical deviation arises from the following factor: opticalcharacteristics do not match between the model affine-invariant regionsand the sample affine-invariant regions due to the difference ofphotographing conditions. When it is assumed that for an observed pixelvalue ν, experimental pixel values are uniformly distributed in [ν−ε,ν+ε], a probability of observing the pixel value ν at the pixel locationx is given by p_(x,A)(ν)/2ε, as shown in FIG. 5 (c).

The next step 310 is to initialize a probability p_(A) that A exists inbranches of the tree structure (repeated subset structure), to 1, for∀A∈S. The next step 315 is to invoke a subroutine of FIG. 6, with inputof S and p_(A). After step 315, step 320 is to output the location ofthe selected interest pixel and threshold of the pixel value forbranching determination, as a correspondence candidate refinementstrategy.

Now let us describe the flowchart of the subroutine of FIG. 6. Learningis started with input of the set S of affine-invariant regions aslearning objects and the existence probability p_(A) for A∈S. First,when in step 600 the number of elements in S is not more than thethreshold, it is determined that there is no need for further divisionand the flow goes to step 605. In step 605, it is impossible toimplement further division into subsets, and thus the input set S ofaffine-invariant regions is determined to be distal segments of the treestructure as it is.

On the other hand, when step 600 is negated, i.e., when the number ofelements in S exceeds the threshold, the flow goes to step 610 to note apixel location x in S and consider division into two subsets S_(L),S_(R) by a threshold θ_(x) of pixel value. If θ_(x) is defined as amedian of pixel value distribution at the pixel location x, the numberof elements in S_(L) is equal to the number of elements in S_(R), andthe depth of the tree structure becomes minimum. In this strategy, θ_(x)is given by the following equation (provided that the pixel values are256 levels of 0-255).

$\begin{matrix}{{\sum\limits_{A \in S_{input}}\; {p_{A}{\int_{0}^{\theta_{x}}{{p_{x,A}(v)}\ {v}}}}} = {\sum\limits_{A \in S_{input}}\; {p_{A}{\int_{\theta_{x}}^{255}{{p_{x,A}(v)}\ {v}}}}}} & \left\lbrack {{Mathematical}\mspace{14mu} {Expression}\mspace{14mu} 2} \right\rbrack\end{matrix}$

Furthermore, a division degree d_(x) is adopted as an index forindicating whether division is good or bad in the case of the subgroupdivision at the pixel location x. The division degree d_(x) iscalculated according to the equation below. The division degree d_(x) is0 in the perfect division case, and is 0.5 in the worst case.

$\begin{matrix}{d_{x} = {\sum\limits_{A \in S}\; {\min \begin{pmatrix}{{p_{A}{\int_{0}^{\theta_{x}}{{p_{x,A}(v)}\ {v}}}},} \\{p_{A}{\int_{\theta_{x}}^{255}{{p_{x,A}(v)}\ {v}}}}\end{pmatrix}}}} & \left\lbrack {{Mathematical}\mspace{14mu} {Expression}\mspace{14mu} 3} \right\rbrack\end{matrix}$

Therefore, the pixel location x_(bar) giving the best division is givenby the equation below.

$\begin{matrix}{\overset{\_}{x} = {\underset{\overset{\_}{x}}{{\arg \mspace{14mu} \min}\mspace{11mu}}\; d_{x}}} & \left\lbrack {{Mathematical}\mspace{14mu} {Expression}\mspace{14mu} 4} \right\rbrack\end{matrix}$

In step 615, if d_(xbar) is not less than a threshold, the division intosubsets S_(L), S_(R) is determined to be impossible and the flow goes tostep 605; otherwise, the flow goes to step 620. In step 605, since it isimpossible to implement further division into subsets S_(L), S_(R), theinput set S of affine-invariant regions is determined to be distalsegments of the tree structure as it is. In step 620, steps 625-645below are repeated for all A∈S.

Step 625 is to determine probabilities p_(L), p_(R) of dividing A intorespective subsets S_(L), S_(R), according to the following equations.It is apparent herein that p_(A)=p_(L)+p_(R).

$\begin{matrix}{{p_{L} = {p_{A}{\int_{0}^{\theta_{x}}{{p_{x,A}(v)}\ {{vd}_{x}}}}}}{p_{R} = {p_{A}{\int_{\theta_{x}}^{255}{{p_{x,A}(v)}\ {{vd}_{x}}}}}}} & \left\lbrack {{Mathematical}\mspace{14mu} {Expression}\mspace{14mu} 5} \right\rbrack\end{matrix}$

Step 630 is to determine whether p_(L) is not less than a predeterminedthreshold; if it is not less than the threshold, step 635 is performedto register A in the subset S_(L) (or to add the subgroup existenceprobability p_(L) corresponding to A, into the subset S_(L)). When step630 is negated (or after step 635), step 640 is performed to determinewhether p_(R) is not less than a predetermined threshold; if it is notless than the threshold, step 645 is performed to register A in thesubset S_(R) (or to add the subgroup existence probability p_(R)corresponding to A, into the subset S_(R)). Step 620 implements thedivision into the subsets S_(L), S_(R) for all A∈S.

Thereafter, when step 640 is negated (or after step 645), step 650 isperformed to determine whether the number of elements in the subsetS_(L) is nonzero. When the number of elements in the subset S_(L) isnonzero, step 655 is performed to recursively invoke the learningsubroutine with input of the subset S_(L) and probability p_(L) (toperform subordinate branching). When step 650 is negated (or after step655), step 660 is performed to determine whether the number of elementsin the subset S_(R) is nonzero. When the number of elements in thesubset S_(R) is nonzero, step 665 is performed to recursively invoke thelearning subroutine with input of the subset S_(R) and probability p_(R)(to perform subordinate branching).

Next, let us describe the flow of implementing the actual refinement ofcorrespondence candidates (collation between the search object image andthe database constructed by learning) in step 705, based on theflowchart of FIG. 7. Step 700 is to extract a plurality ofaffine-invariant regions (standard regions) from a sample image. Thiscan be implemented by a technique equivalent to step 300 in theflowchart of FIG. 3 for learning. The next step is to calculatecorrespondence candidates with the use of the learned refinementstrategy (database), for each affine-invariant region (standard region)extracted from the search object image. In practice, the selection ofthe interest image (selection of the specific region) according to therefinement strategy and the branching selection using the comparisonbetween pixel value and threshold are repeated down to the distalsegments of the tree structure. This implements matching between thestandard regions of the model images and the standard regions of thesearch object image.

INDUSTRIAL APPLICABILITY

Since the image search method or image search apparatus of the presentinvention involves the repeated classifications of the model images intothe subsets on the basis of the specific region in the standard regions,it is thus able to construct the image database permitting the robustand fast search. When an object image is given as a search object, theforegoing database is used to perform the robust and fast search, usingthe specific region in the standard region in the object image.

1: An image search method comprising: a normalization step of extractinga plurality of regions from one or more model images and normalizing theregions as standard regions; a classification step of setting a specificregion in each normalized standard region and classifying the pluralityof standard regions under two or more subsets on the basis of a featureof the specific region; a recursive classification step of iterativelyperforming an operation of setting an other specific region at alocation different from that of said specific region in each standardregion classified in each subset and classifying the plurality ofstandard regions under still more subsets on the basis of a feature ofthe other specific region; and an output step of outputting thelocations of the specific regions in the standard regions in therespective classifications and the features of the specific regions inthe classifications. 2: The image search method according to claim 1,wherein the features of the specific regions used in the classificationsare luminance information about the specific regions. 3: The imagesearch method according to claim 1, wherein the normalization stepcomprises dividing a standard region into a background part and a searchpart, and wherein when the location of the specific region in theclassification step or in the recursive classification step is in thebackground part, the specific region is included in all the subsets inthe classification. 4: The image search method according to claim 1,wherein the normalization step comprises normalizing a region possessingsuch a property that a shape can be normalized regardless of an affinetransformation thereof, as a standard region. 5: The image search methodaccording to claim 1, wherein the normalization step comprisesnormalizing a region possessing such a property that a shape can benormalized regardless of a rotation transformation thereof, as astandard region. 6: The image search method according to claim 1,further comprising: an input step of inputting a predetermined regionresulting from normalization of a region extracted from a search object,as a detection object; and a search step of performing a search todetermine to which terminal subset the predetermined region belongs,based on an output result in the output step, and thereby finding acorrespondence between the predetermined region and a subset of thestandard regions. 7: The image search method according to claim 6,wherein the search step comprises finding the correspondence between thepredetermined region and the subset of the standard regions, inconsideration of a location deviation from the standard region in thenormalization for the predetermined region in the input step. 8: Animage search apparatus comprising: normalization means for extracting aplurality of regions from one or more model images and normalizing theregions as standard regions; classification means for setting a specificregion in each normalized standard region and classifying the pluralityof standard regions under two or more subsets on the basis of a featureof the specific region; recursive classification means for iterativelyperforming an operation of setting an other specific region at alocation different from that of said specific region in each standardregion classified in each subset and classifying the plurality ofstandard regions under still more subsets on the basis of a feature ofthe other specific region; and output means for outputting the locationsof the specific regions in the standard regions in the respectiveclassifications and the features of the specific regions in theclassifications. 9: The image search apparatus according to claim 8,wherein the features of the specific regions used in the classificationsare luminance information about the specific regions. 10: The imagesearch apparatus according to claim 8, wherein the normalization meansdivides a standard region into a background part and a search part, andwherein when the location of the specific region in the classificationby the classification means or in the recursive classification by therecursive classification means is in the background part, the specificregion is included in all the subsets in the classification. 11: Theimage search apparatus according to claim 8, wherein in thenormalization by the normalization means a region possessing such aproperty that a shape can be normalized regardless of an affinetransformation thereof, is normalized as a standard region. 12: Theimage search apparatus according to claim 8, wherein in thenormalization by the normalization means a region possessing such aproperty that a shape can be normalized regardless of a rotationtransformation thereof, is normalized as a standard region. 13: Theimage search apparatus according to claim 8, further comprising: inputmeans for inputting a predetermined region resulting from normalizationof a region extracted from a search object, as a detection object; andsearch means for performing a search to determine to which terminalsubset the predetermined region belongs, based on an output result bythe output means, and thereby finding a correspondence between thepredetermined region and a subset of the standard regions. 14: The imagesearch apparatus according to claim 13, wherein in the search by thesearch means the correspondence between the predetermined region and thesubset of the standard regions is found in consideration of a locationdeviation from the standard region in the normalization for thepredetermined region in the input step.